signal processor
Recently Published Documents





Nano Today ◽  
2022 ◽  
Vol 42 ◽  
pp. 101352
Chi Yao ◽  
Jianpu Tang ◽  
Chenxu Zhu ◽  
Sen Yang ◽  
Han Tang ◽  

Usthulamuri Penchalaiah ◽  
V. G. Siva Kumar

Digital Signal Processors (DSP) have a ubiquitous presence in almost all civil and military signal processing applications, including mission critical environments like nuclear reactors, process control etc. Arithmetic and Logic units (ALU), being the heart of any digital signal processor, play critical and decisive roles in achieving the required parameter benchmarks and the overall efficiency and robustness of the digital signal processor. State of the art research has shown successful traction with the performance requirements of critical Multiply-Accumulate (MAC) parameters, like reduced power consumption, small electronic real estate footprint and reduction in delay with the associated design complexity. Judicious placement of its building blocks, namely, the truncated multiplier and half-sum carry generation-sum carry generation (HSCG-SCG) adder in the architectural design of ALU and the type of adder and multiplier circuits selected are the core decisions that decide the overall performance of the ALU. To overcome the drawback and to improve the performance further, this work proposes a new architecture for the square root (SQRT) carry select adder (CSLA) using half-sum generation (HSG), half-carry generation (HCG), full-sum generation (FSG) and full-carry generation (FCG) blocks. The proposed design contains N-bit architecture, and comparative results are considered for 8-bit, 16-bit and 32-bit combinations. All the designs are implemented in the Xilinx ISE environment and the results show that better area, power, and delay performance compared to the state of art methods.

2021 ◽  
Stefan A. Damjancevic ◽  
Samuel Ajay Dasgupta ◽  
Emil Matus ◽  
Dmitry Utyanksy ◽  
Pieter van der Wolf ◽  

2021 ◽  
Vol 30 (5) ◽  
pp. 986-990
YU Xin ◽  
LU Wu ◽  
LI Xiaolong ◽  
LIU Mohan ◽  
WANG Xin ◽  

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (5) ◽  
pp. 86-93
V. V. Kliuchenia

The hardware implementations of fixed-point DCT blocks, known as IntDCT [1] and BinDCT [2], require some solutions. One of the main issues is the choice between the implementation of the conversion on FPGA, or the implementation on a digital signal processor (Digital Signal Processor, DSP). Each of the implementations has its own pros and cons. One of the most important advantages of the DSP implementation is the presence of special instructions used in DSP, in particular, the ability to multiply two numbers in one clock cycle. Therefore, with the advent of DSP, the limitation on the number of multiplications in algorithms was removed. On the other hand, when implementing a block on an FPGA, we can limit not ourselves to the bitness of the data (within reasonable limits), we have the ability to parallelize all incoming data and implement specialized computing cores for various tasks. In fact, designing multimedia systems on FPGAs reminds the design of similar systems based on the logic of a small and medium degree of integration. Such an implementation has the same limitations: a relatively small amount of available memory, the need to design basic structural elements (multipliers, divisors), etc. It is the inequality of the addition and multiplication operations when they are implemented on FPGAs that caused the search for DCT algorithms with the smallest number of factors. However, even this is not enough, since the structure of the multiplier is many times more complex than the structure of the adder, which made it necessary to look for ways to transform without using multiplications at all. This article shows how, on the basis of integer direct and inverse DCT and distributed arithmetic, to create a new universal architecture of decorrelated transform on FPGAs without multiplication operations for image transformation coding systems that operate on the principle of lossless-to-lossy (L2L), and to obtain the best experimental results in terms of hardware resources compared to comparable compression systems.

Sign in / Sign up

Export Citation Format

Share Document